coef.lme: Extract lme Coefficients
In nlme: Linear and Nonlinear Mixed Effects Models

Description Usage Arguments Value Author(s) References See Also Examples

The estimated coefficients at level i are obtained by adding together the fixed effects estimates and the corresponding random effects estimates at grouping levels less or equal to i. The resulting estimates are returned as a data frame, with rows corresponding to groups and columns to coefficients. Optionally, the returned data frame may be augmented with covariates summarized over groups.

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## S3 method for class 'lme'
coef(object, augFrame, level, data, which, FUN, 
       omitGroupingFactor, subset, ...)

`object`	an object inheriting from class `"lme"`, representing a fitted linear mixed-effects model.
`augFrame`	an optional logical value. If `TRUE`, the returned data frame is augmented with variables defined in `data`; else, if `FALSE`, only the coefficients are returned. Defaults to `FALSE`.
`level`	an optional positive integer giving the level of grouping to be used in extracting the coefficients from an object with multiple nested grouping levels. Defaults to the highest or innermost level of grouping.
`data`	an optional data frame with the variables to be used for augmenting the returned data frame when `augFrame = TRUE`. Defaults to the data frame used to fit `object`.
`which`	an optional positive integer or character vector specifying which columns of `data` should be used in the augmentation of the returned data frame. Defaults to all columns in `data`.
`FUN`	an optional summary function or a list of summary functions to be applied to group-varying variables, when collapsing `data` by groups. Group-invariant variables are always summarized by the unique value that they assume within that group. If `FUN` is a single function it will be applied to each non-invariant variable by group to produce the summary for that variable. If `FUN` is a list of functions, the names in the list should designate classes of variables in the frame such as `ordered`, `factor`, or `numeric`. The indicated function will be applied to any group-varying variables of that class. The default functions to be used are `mean` for numeric factors, and `Mode` for both `factor` and `ordered`. The `Mode` function, defined internally in `gsummary`, returns the modal or most popular value of the variable. It is different from the `mode` function that returns the S-language mode of the variable.
`omitGroupingFactor`	an optional logical value. When `TRUE` the grouping factor itself will be omitted from the group-wise summary of `data` but the levels of the grouping factor will continue to be used as the row names for the returned data frame. Defaults to `FALSE`.
`subset`	an optional expression specifying a subset
`...`	some methods for this generic require additional arguments. None are used in this method.

a data frame inheriting from class "coef.lme" with the estimated coefficients at level level and, optionally, other covariates summarized over groups. The returned object also inherits from classes "ranef.lme" and "data.frame".

José Pinheiro and Douglas Bates bates@stat.wisc.edu

Pinheiro, J. C. and Bates, D. M. (2000), Mixed-Effects Models in S and S-PLUS, Springer, New York, esp. pp. 455-457.

lme, ranef.lme, plot.ranef.lme, gsummary

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fm1 <- lme(distance ~ age, Orthodont, random = ~ age | Subject)
coef(fm1)
coef(fm1, augFrame = TRUE)

    (Intercept)       age
M16    16.57335 0.5913314
M05    15.58444 0.6857855
M02    16.03361 0.6746930
M11    17.65160 0.5413593
M07    16.15314 0.6950852
M08    17.62141 0.5654490
M03    16.58721 0.6960375
M12    15.76312 0.7747492
M13    12.63157 1.0738537
M14    17.66546 0.6460654
M09    16.31672 0.7960938
M15    16.22614 0.8683628
M06    17.97875 0.7433765
M04    19.76156 0.5943004
M01    17.81269 0.8758697
M10    19.41435 0.8713318
F10    14.47973 0.4095945
F09    16.47016 0.4421435
F06    16.14053 0.4736282
F01    16.27515 0.4819755
F05    17.27792 0.4922276
F07    16.57335 0.5913314
F02    15.74926 0.6700431
F08    18.01143 0.4857849
F03    15.98832 0.7108275
F04    17.83027 0.6303230
F11    17.97875 0.7433765
    (Intercept)       age distance    Sex
M16    16.57335 0.5913314   23.000   Male
M05    15.58444 0.6857855   23.000   Male
M02    16.03361 0.6746930   23.375   Male
M11    17.65160 0.5413593   23.625   Male
M07    16.15314 0.6950852   23.750   Male
M08    17.62141 0.5654490   23.875   Male
M03    16.58721 0.6960375   24.250   Male
M12    15.76312 0.7747492   24.250   Male
M13    12.63157 1.0738537   24.250   Male
M14    17.66546 0.6460654   24.875   Male
M09    16.31672 0.7960938   25.125   Male
M15    16.22614 0.8683628   25.875   Male
M06    17.97875 0.7433765   26.375   Male
M04    19.76156 0.5943004   26.625   Male
M01    17.81269 0.8758697   27.750   Male
M10    19.41435 0.8713318   29.500   Male
F10    14.47973 0.4095945   18.500 Female
F09    16.47016 0.4421435   21.125 Female
F06    16.14053 0.4736282   21.125 Female
F01    16.27515 0.4819755   21.375 Female
F05    17.27792 0.4922276   22.625 Female
F07    16.57335 0.5913314   23.000 Female
F02    15.74926 0.6700431   23.000 Female
F08    18.01143 0.4857849   23.375 Female
F03    15.98832 0.7108275   23.750 Female
F04    17.83027 0.6303230   24.875 Female
F11    17.97875 0.7433765   26.375 Female